Feature selection using modified ant colony optimization for wireless capsule endoscopy

In this study, a modified ant colony optimization algorithm has been proposed to find a feature subset most relevant to the classification task. The algorithm incorporates a new heuristic information component based on classification accuracy. The proposed methodology has been applied in a multiclass classification problem of capsule endoscopic images, where image regions will be classified as bleeding, non-bleeding and uninformative regions. 75 dimensional features extracted from five color spaces have been investigated in the experiments. The proposed MACO algorithm efficiently finds the optimum feature subset over the five color spaces including RGB, HSV, Lab, YCbCr, and CMYK, resulting in a feature subset outperforming those obtained individually from each color space. The comparative study with state-of-the-art methods of feature selection demonstrated that MACO can provide the most relevant features and improve the performance in terms of accuracy, sensitivity and computational time.